Optimization_of_process_variables.doc

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Glycerol Production by Hydrogenolysis of Sucrose: Optimization of

(Ni, W, Cu)/Kieselguhr Catalyst by Response Surface Methodology and its

Characterization. Tanuja Srivastava1, and Dharmesh C. Saxena2*

1. Bhai Gurdas Institute of Engineering and Technology, Sangrur-148001,Punjab,India

2. Dept. of Food Technology, SLIET,Longowal-148 106. (Punjab) (INDIA).

Abstract: Polyalcohols have wide industrial and other usages. Presently, they are being mainly produced from petroleum and its products. Sucrose hydrogenolysis is industrially important for the production of polyols. To provide high glycerol yield, a nickel catalyst promoted by tungsten and copper supported on kieselguhr was used in the optimization of process variables viz., temperature, pressure, and sucrose and catalyst concentration using Response Surface Methodology. Response Surface Methodology (RSM) is an effective tool to optimize the process and process variables. A 4 X 5 experimental design has been adopted to study the effect of process variables on glycerol yield. A linear second-order model has been developed to optimize and to study the interaction effects on glycerol yield in the catalytic hydrogenolysis of sucrose. Maximum glycerol yield of 43.85% was identified at 161°C, 48.4 atm pressure, 14.8% sucrose concentration, and 12.23% catalyst concentration. The catalyst obtained by the optimized process has been characterized by the electron microscopy, X-ray diffraction, and magnetic measurement techniques.

Keywords: Hydrogenolysis, Sucrose, Catalysis, RSM, Optimization, Glycerol

INTRODUCTION

Hydrogenolysis of regenerative source such as sucrose to produce industrially important polyalcohols generates considerable interest. Sucrose hydrogenolysis is industrially important for the production of glycerol, ethylene glycol, and propylene glycol. Petroleum and its products are currently mainly producing these polyalcohols. There is a need of catalyst that provides high product yields under milder reaction conditions. Catalyst preparation procedure affected the final catalyst and enhanced the catalyst activity and selectivity considerably [1-4]. Li et. al. [5] have used Ni-P amorphous alloy catalyst to produce sorbitol by glucose hydrogenation. However, the product distribution obtained by sucrose hydrogenolysis heavily depends upon the process conditions. Conradin [6] obtained glycerol by sucrose hydrogenolysis at 180-250°C and 61 atm pressure. Vasilakos et. al. [7] described a three stage catalytic process involving Ni, W, and Cu/ kieselguhr catalyst. Sohounlove et. al. [8] hydrogenolyzed sorbitol at 210°C and 80 atmospheric pressure in the presence of Ru/SiO2 catalyst to produce glycerol ethylene glycol, propylene glycol and glycerol. Muller [9] described sucrose hydrogenolysis in the presence of 5% Ru/Cu catalyst. Tronconui et. al. [10] developed a technology for obtaining ethylene glycol and propylene glycol from catalytic hydrogenolysis of sorbitol. The overall picture of the hydrogenolysis of sucrose is quite complex. A large number of consecutive and parallel reactions are involved. The use of a catalyst under appropriate conditions may produce the desired product mixture. The role and influence of operating conditions could be used to develop a mechanistic kinetic model to account for the observed reagent conversions and product selectivity. Watterman and Tusenbroch [11] used nickel sulphate to precipitate nickel on kieselguhr and investigated the effect of heat treatment on the activity of the resulting catalyst. Effects of H2S partial pressure on the catalytic activity and product selectivity were investigated over the sulfided NiMo/Al2O3 and NiW/ Al2O3

catalysts [12]. Response surface methodology (RSM) is an effective tool to optimize the process variables with

minimum number of experimental run. An experimental design such as the central composite rotatable design (CCRD) to fit a model by least square technique has been selected during the studies. If the proposed model is adequate, as revealed by the diagnostic checking provided by an analysis of variance (ANOVA), the 3-D plots and contours can be usefully employed to study the response surface and locate the optimum. The basic principle of RSM is to relate product properties of regression equations that describe interrelations between input parameters and product properties [13]. The present work provides studies on the effect of various process variables on the hydrogenolysis of sucrose with an aim of obtaining milder reaction conditions and simultaneously maximizing yield of glycerol, the most expensive polyol among those obtained during the reaction. The various process variables studied were temperature, pressure, sucrose concentration, and catalyst concentration. It is apparent from the literature that the use of RSM for catalyst synthesis in hydrogenolysis process is rare. So the values to optimize the amounts of catalyst constituent to maximize yields of glycerol used by Tanuja et. al. [24] have been used. The information on the structure of supported metal catalysts provides an insight into the connection between the parameters of catalyst’s constituents and the catalyst performance. Catalyst characterization, i.e. the investigation of relevant aspects of catalyst structure may also be required for quality control. The techniques of electron microscopy and X-ray diffraction have been used for obtaining information regarding the microstructure and interplanar spacing in the catalyst [14-16].Tanuja et.al. [25] have used Ni,Mo,Cu / kieselguhr catalyst for process optimization for hydrogenolysis of sucrose. The work in this field is scanty. The outcome of this work would pave the way for developing a commercial scale process for production of glycerol and polyols from regenerative source like sucrose using Ni,W,Cu / kieselguhr catalyst.2. EXPERIMENTAL2.1 MaterialsKieselguhr was obtained from S.D. Fine Chemicals, Mumbai (India). Analytical grade salts of nickel, copper, and tungsten

© JGRCS 2010, All Rights Reserved 1

Main Author’s Name et al, Journal of Global Research in Computer Science,

as well as sodium carbonate and ammonium hydroxide were used for catalyst preparations. Laboratory grade sucrose (Qualigens, Mumbai, India) and high purity hydrogen (Modi Gases, New Delhi, India) were used for the hydrogenolysis reaction. Analytical grade sucrose, D-glucose, fructose, sorbitol, ethylene glycol, propylene glycol, and glycerol (Qualigens, Mumbai, India) were used for reference samples. The product were analyzed using IATROSCAN TLC/FID analyzer, wherein analytical grade chloroform, methanol and HPLC grade water (Qualigens, Mumbai, India) were used as solvent and analytical grade boric acid (Qualigens, Mumbai, India) was used to impregnate the TLC rods. 2.2 Methods2.2.1 Experimental Design:The point at which glycerol yields maximum were selected as a center points for each variable range in the experimental design.

Tab. 1 Values of Coded Levels and Equations Relating Actual xi and

Coded Xi Ratios

IndependentVariables

Unit Symbols Levels

Coded Actual -2 -1 0 +1 +2

CatalystConcentration

% X1 x1 7.5 10 12.5 15 17.5

SucroseConcentration

% X2 x2 5 10 15 20 25

PRESSURE Atm X3 x3 30 40 50 60 70

Temperature °C X4 x4 130 150 170 190 210

Where X1 = (x1 -12.5)/2.5; X2 = (x2-15)/5;

X3 = (x3 -50)/10; X4 = (x4-170)/20

Yield of glycerol was the only response (Y) measured in the study. The experimental region extended from –2 to 2 in terms of the coded independent variables Xi. The coding facilitated the computations for regression analysis and optimum search. The increments of variation for each variable spaced around the centre-point ratios, along with equations relating actual and coded ratios are presented in “(see Tab. 1)”. The range of experimental design (actual values) was decided based on the preliminary studies. By substitution in these equations, catalyst compositions were

coded for solutions of the multiple - regression (prediction) equations.

Tab. 2 Central composite design

arrangement and response

Experiment No.

Variable Levels Response

X1 X2 X3 X4 Yield (%)

1 -1 -1 -1 -1 36.20

2 -1 -1 -1 +1 36.30

3 -1 -1 +1 -1 36.20

4 -1 -1 +1 +1 34.30

5 -1 +1 -1 -1 33.70

6 -1 +1 -1 +1 31.56

7 -1 +1 +1 -1 31.50

8 -1 +1 +1 +1 29.75

9 +1 -1 -1 -1 30.54

10 +1 -1 -1 +1 30.20

11 +1 -1 +1 -1 28.65

12 +1 -1 +1 +1 28.10

13 +1 +1 -1 -1 29.75

14 +1 +1 -1 +1 30.30

15 +1 +1 +1 -1 29.71

16 +1 +1 +1 +1 28.89

17 -2 0 0 0 30.40

18 +2 0 0 0 26.70

19 0 -2 0 0 32.10

20 0 +2 0 0 29.43

21 0 0 -2 0 31.60

22 0 0 +2 0 29.15

23 0 0 0 -2 38.93

24 0 0 0 +2 32.18

25 0 0 0 0 45.50

26 0 0 0 0 44.80

27 0 0 0 0 45.50

28 0 0 0 0 45.80

29 0 0 0 0 44.90

30 0 0 0 0 45.20

A central composite rotatable design (CCRD) was adopted, as shown in “(see Tab. 2)”. This design was specifically suited for analysis with second order polynomials [13]. The CCRD combined the vertices of a hypercube whose coordinates are given by the 2nd factorial design (runs 1-16)



with the 'star or axial' points (runs 17-24). The star points have all of the factors set to 0, the midpoint, except one factor, which has the value +/- Alpha (in case of 4 factors, the value of alpha is 2). The star points were added to the factorial design to provide for estimation of curvatures of the model [13]. Seven replicate experiments (runs 25-30), at the centre of the design, were performed. In earlier studies, co-author randomised the experiments in order to minimize the effects of unexplained variability in the observed responses due to extraneous factors [17]. A similar approach was implemented in the present study.For analysis of the experimental design by RSM, it is assumed that a mathematical function, fk, exists for a response variable Yk, in terms of ‘m’ independent processing factors, xi (i =1, 2, 3,.........., m), such as [18]:

Yk = fk (x1, x2, .........., xm) ------------------ (1)In our case, m=4

Y= Glycerol Yield (%)x1= Temperature (°C)x2= Pressure (Atm.)x3= Sucrose Concentration (%)x4= Catalyst Concentration %)

The unknown function, fk, was assumed to be represented approximately by a second-degree polynomial equation:

where bko is the value of the fitted response at the centre point of the design i.e. (0,0,0,0), bki, bkii, and bkij are the linear, quadratic and cross-product regression terms, respectively.2.3 Analysis of Data

The regression analysis for fitting the model represented by Equation 2 to experimental data, analysis of variance, maximization of the polynomial thus fitted, and mapping of the fitted response surfaces was achieved using a statistical package (Design Expert-6.0.5, Stat-Ease Inc., 2021 East Hennepin Ave., Suite 191, Minneapolis, MN 55413). The response surfaces and the corresponding contour plot for this model were plotted as a function of two variables, while keeping the other variables at an optimum value.

2.4 EXPERIMENTAL PROCEDURE

As optimized earlier [19], nickel (29.80 %), tungsten (10.0 %), and copper (1.07 %) were co-precipitated on kieselguhr using a Heidolph rotary vacuum evaporator with electronic temperature agitation and control and incorporating various attachments and fittings. The catalyst has been reduced using 47cm long stainless steel reactor tube of 2.5cm dia housed in a ceramic tube of 6cm dia the surface of which is wounded with nicrome wire for heating. A sample of 5 gm unreduced catalyst was filled in the reactor and heated up to 600°C. At this temperature, hydrogen gas was passed through the reactor at constant flow rate for 2 hours. The reduced catalyst was then taken out quickly into a beaker filled with water and the resulting slurry was transferred to Parr reactor for hydrogenolysis.The reaction was carried out in a microprocessor controlled 450 ml high pressure Parr reactor assembly (USA) as per the experimental design. The reaction time of 45 min. was selected based on the preliminary studies wherein the data were collected up to 240 min and the catalyst did not show any marked changes in the mechanism of sucrose hydrogenolysis after 45 min. The technique of thin-layer chromatography coupled with flame ionization

detector was used to analyze the products of hydrogenolysis of sucrose. 2.5 Characterization of the optimized catalyst

Physicochemical properties of the catalyst were studied using techniques of electron microscopy, X-ray diffraction, and magnetic measurements under following conditions:Electron Microscopy: The activated catalyst was powdered and suspended in double distilled water. The suspension was allowed to settle for about an hour. A drop from the suspension was carefully placed on 200-mesh carbon coated copper grids. After drying, the grid was examined under a JEOL GEM 2000 FX transmission electron microscope having a resolution of 1.5A° and operating at 120 KV. Gold diffraction pattern was used as a standard for interpreting the diffraction patterns. X-ray diffraction technique: The finely powdered samples of catalyst were mounted individually on SEIFERT (JSO DEBYE FLEX 2002) X-ray generator. The X-ray data were recorded at a scanning speed of 1.2° per minute (2 ) between = 10° and 62°. The monochromatic beam of Cuk

radiation was used. The diffractometer was operated at a count rate of 5K CPM and a time constant of 10 sec. The scanning slit was fixed at 2 mm and the receiving slit at 0.3 mm. The X-ray tube was driven at 30 KV and 20 mA. Diffracted intensities were measured by means of a scintillating column at a chart speed of 30 mm/min. The correction for instrumental breadth of diffraction peak under identical conditions was evaluated using standard aluminium samples annealed for 4 hours at 300°C. Magnetic measurements: Finally powdered catalyst samples were pressed in cylindrical pellets (3 mm dia and 3 mm ht.) in inert atmosphere to avoid oxidation. A polymer coating was applied to the surface in order to impart mechanical strength to the pellets and to prevent further oxidation of the reduced catalyst. The measurements were carried out at room temperature using a Varian V-7200 series 9-inch electromagnet and a parallel field vibrating samples magnetometer (Model 150 A PARC, USA).3. RESULTS AND DISCUSSION3.1 Diagnostic Checking of the Fitted Model

Regression analysis for the model indicated that the fitted quadratic model accounted for 98.8 % of the variations in the experimental data. A multiple regression equation was generated relating the percentage yield of glycerol to coded levels of the variables. The model developed is as follows:Glycerol yield (Y) =



Fig.1 Standard Pareto chart for the estimated effects of the

model

All main effects, linear and quadratic, and interaction of effects were calculated for the model. The estimated effects were used to plot a standardized Pareto chart for the model “Figure 1”; the chart consists of bars with lengths proportional to the absolute values of the estimated effects divided by their standard errors. The chart includes a vertical line at the critical t- value for a 99% confidence level. A bar crossing this vertical line corresponds to a factor or combination of factors that has a significant effect in the response. Tab. 3 Estimated Coefficients of the Fitted Quadratic Equation for the Response based on t-statistic

Coefficients Estimated coefficients t-

statistic

X0 43.28 161.22

X1 -0.59** -2.37

X2 -0.70** -3.15

X3 -0.84** -4.20

X4 -1.95** -12.37

X12 -4.13** -30.52

X22 -3.58** -26.16

X32 -3.68** -26.93

X42 -2.38** -16.75

X12 -0.20NS 0.80

X13 -0.09NS 1.45

X14 0.28NS 3.70

X23 0.03NS 2.20

X24 0.04NS 2.21

X34 1.10** 8.63

R2 (adj) 0.9885

NS – Not Significant; *P<0.05 (2.145); **P<0.01 (2.977); Degrees of freedom = 14

The regression coefficients are shown in “(see Tab. 3)”, as well as the coefficient of determination (an estimate of the fraction of overall variation in the data accounted for by the model) (R2 = 0.988) obtained for the model. The temperature of the reaction had highly significant negative linear and quadratic effect on glycerol yield at 99% level. The similar effects were found for the pressure in the reaction and the sucrose concentration on the glycerol yield. The catalysis concentration had non-significant linear but significant negative quadratic effect on glycerol yield at 99% level. However, the effect of temperature was more pronounced followed by sucrose concentration and pressure in the reaction as evident by the magnitude of the coefficient in the model. The only interaction of temperature and pressure was observed to affect significantly the glycerol yield.

3.2 Analysis of varianceTab .4 Analysis of Variance for the Model

Sources of Variation

Sum of Squares

Degree of freedom

Mean Square

F Value Prob > F

Regression 1115.11 14 79.65 181.64

< 0.0001

X1 8.45 1 8.45 21.06 0.0006

X2 11.75 1 11.75 28.49 0.0001

X3 17.05 1 17.05 40.46< 0.0001

X4 91.53 1 91.53 208.43< 0.0001

X12 468.95 1 468.95 1059.6

3

< 0.0001

X22 351.68 1 351.68 795.15

< 0.0001

X32 371.10 1 371.10 838.93

< 0.0001

X42 155.87 1 155.87 353.54

< 0.0001

X12 0.64 1 0.64 3.44 0.2495

X13 0.14 1 0.14 2.31 0.5889



X14 1.28 1 1.28 4.89 0.1097

X23 0.02 1 0.02 2.04 0.8447

X24 0.02 1 0.02 2.05 0.8331

X34 19.47 1 19.47 45.92< 0.0001

Residual 6.65 15 0.44

Lack of Fit 5.90 10 0.59 5.94NS 0.0715

Pure Error 0.75 5 0.15

Total 1121.76 29

NS – Not Significant

When a model has been selected, an Analysis of Variance is calculated to assess how well the model represents the data. An Analysis of Variance for the response is presented in “(see Tab. 4)”. To evaluate the goodness of the model, the Coefficient of Variation (the ratio of the standard error of estimate to the mean value expressed as a percentage) and F-value tests are conducted. The F distribution is a probability distribution used to compare variances by examining their ratio. If they are equal then the F value would equal 1. The F value in the ANOVA table is the ratio of model mean square (MS) to the appropriate error mean square. The larger the ratio, the larger the F value and the more likely that the variance contributed by the model is significantly larger than random error. As a general rule, the Coefficient of Variation should be not greater than 10% [20]. In the present case, the coefficient of variation for glycerol yield was 2.06%. Also, the F-value for response was significant at 99%. On the basis of Analysis of Variance, the conclusion is that the selected model adequately represents the data for glycerol yield.

-0.90

-0.60

-0.30

0.00

0.30

0.60

0.90

20.00 25.00 30.00 35.00 40.00 45.00

Resid

uals

Model Values

Figure.2

Fig.2 Standard residuals between experimental values and values predicted by the surface response model vs. model values

A diagnostic plot for the response is shown in “Figure 2”. From analysis of residuals it is possible to conclude that they are randomly distributed around zero and there is no evidence of outliers (no point lying away from the mean more than four times the standard deviation).

3.3 Conditions for optimum responsesTable 5. Optimum Conditions for Percentage Yield of Glycerol

IndependentVariables

Unit

Coded Values

Actual Values

CatalystConcentration % -0.11 14.23

SucroseConcentration % -0.04 16.80

Pressure Atm -0.16 50.4

Temperature °C -0.45 163.0

Maximum Glycerol Yield (%) 43.85

Models were useful in indicating the direction in which to change variables in order to maximize glycerol yield. The optimum conditions to yield maximum glycerol are presented in “(see Tab. 5)”. The model provides the information about the influence of each variable on the glycerol yield in the catalytic hydrogenolysis of sucrose. However, these are the optimized conditions that provide the information to produce maximum yields of glycerol.

Optimum values of glycerol yield for all variables lie exactly in the middle of the experimental range, indicating the validity of the selection of the variables range. The response surfaces and the corresponding contour graphs in “Figure 3” are based on the above model (Y), keeping two variable at the optimum level and varying the other two within the experimental range.

The surface plot of glycerol yield as a function of temperature and pressure demonstrated the maximum yield at an optimum 161°C temperature and 48.4 atm pressure “Figure 3-A”. Increasing or decreasing any parameter from the optimum concentration resulted in decreased yield. This may be due to the fact that below optimum (161°C) sucrose hydrolysed into glucose and fructose whose simultaneous hydrogenation produced sorbitol. Glycerol and glycols were obtained by hydrogenolysis of sorbitol. However, at optimum there was a change in mechanism of sucrose conversion and sucrose directly yielded glycerol and glycol by its hydrogenolysis. Sorbitol was then being produced by hydrogenation of glucose and fructose once they had been formed by sucrose hydrolysis. At higher temperature than optimum, the decrease in yield may be attributed to the formation of some unidentified products whose peaks were detected above glycerol and glycol peaks on the thin-layer chromatogram (unpublished). Nickel nitrate reacts with silica during precipitation to form nickel hydrosilicates thus decreasing silica amount and correspondingly its X-ray diffraction peak height because peak height of a component is proportional to its amount present [21]. Therefore, it may be concluded that by increasing temperature, the reaction between nickel nitrate and silica facilitated resulting in the decrease in peak height and afterwards constant. This is



expected, as increased reaction rate at higher temperature caused more nickel to precipitate thus increasing the nickel peak height. The similar effect was observed by other workers in the preparation of nickel catalyst supported on rice husk [22]. At lower pressure sucrose hydrogenolysis predominates over its hydrolysis reverses at higher pressures with predominance of sucrose hydrolysis.

The response of glycerol yield affected by pressure and sucrose concentration is shown in “Figure 3-B”. Sucrose concentration in the solution higher than optimum may increase resistance to hydrogen diffusion at the reaction sites. This may be the cause of decreased sucrose conversion rate at its concentrations more than the optimum.

Fig.3 Response surface and contour graphs of glycerol yield affected by different process variables in the catalyst synthesis Fig.3-A. Process variables - Temperature and Pressure Fig.3-B. Process variables - Pressure and Sucrose concentration Fig.3-C. Process variables - Sucrose and Catalyst concentration

The glycerol yield was found to be increased till the optimum catalyst loading of 12.23% and then after it decreased as evident in “Figure 3-C”. This is expected because breaking of ether linkage and hydrogenation of an unsaturated bond requires much less activation energy than breaking of other bonds of sucrose or monosaccharides.The catalyst with optimum process parameters has been synthesized using the method of coprecipitation as described above. With the optimized variables, the hydrogenolysis of sucrose gave glycerol yield of 43.85%, which was almost near to the maximum yield obtained by the optimization procedure. There is an indirect correlation of physicochemical properties of the catalyst for glycerol production. However, the glycerol yield is directly affected by cleavage of sucrose molecule in presence of the catalyst. Therefore, the results could be better explained on the basis of sucrose molecule cleavage instead of catalyst because the catalyst’s properties viz., the amounts of constituents (Ni, W, Cu supported upon kieselguhr) as well as precipitating conditions of catalyst were same. The characterization of the optimised catalyst using SEM, TEM, XRD and magnetic measurements has been done to find interplanar spacing of the component present in the catalyst and to assess metal dispersion and relative amounts of various components using X-ray line broadening and ratios of peak areas, respectively during study of effect of process variables on glycerol production. Arai et. al. [23] examined the state of nickel on the support by XRD and SEM/X-ray microanalysis and found the activation of nickel by pre-treatment of the support due to the increase of those nickel atoms which dispersed finely over the support by the increased number of surface hydroxyl groups. The

physicochemical properties of this catalyst studied by these techniques are as follows:

Fig.4 Scanning electron micrograph of Kieselguhr particles

A) Electron Microscopy: The scanning electron micrograph of kieselguhr particles in “Figure 4” shows round sharp edged particles having broad size distribution with larger fraction of smaller particles above or below 5M.

Fig.5 Transmission electron micrograph of Kieselguhr particlesIn the same way, transmission electron micrograph in “Figure 5” reveals the presence of sharp edged, round intermingled kieselguhr particles. Interplanar spacing corresponding to this pattern (2.5 M) matches with those obtained from X-ray diffraction peaks of the same material. These peaks show the presence of SiO2 low or -cristobalite, SiO2 tridymite, SiO2 kealite silica k, SiO2 Nelanophlogite and -Fe2O3 iron oxide components in the kieselguhr.

Fig.6 Electron diffraction pattern of Kieselguhr particles from

the area shown in Fig. 5

The electron diffraction obtained from the same region is shown in “Figure 6”.



Fig.7 Electron diffraction pattern of gold filmAn electron diffraction pattern of gold has been used as a standard for the electron diffraction studies shown in “Figure 7”.

Fig.8 Transmission electron micrograph of unreduced catalyst showing fibrous stateAs evident from “Figure 8” of the unreduced catalyst, the coprecipitation of the catalyst seems to change the morphology of support particles from sharp edged particles into fibrous state. However, on reduction, these fibres were found to be covered with catalyst particles. Study shown elsewhere that the spotty character of nickel diffraction lines (111), (200), (220) and (311) on the electron diffraction pattern bears that those nickel crystallites are indeed large24.

LEGENDS FOR THE FIGURE 1 – Activated catalyst; 2- Spent catalyst

Fig. 9 Nickel (111) peaks in X-ray diffraction patterns of activated and spent catalysts

B) X-ray diffraction pattern: “Figure 9” shows that Ni (111) diffraction peak much reduced in intensity and width in case of spent (re-used) catalyst in comparison to that of

freshly reduced one. Except (101) peak of -cristobalite silica, all kieselguhr peaks also disappeared. The decreased Ni peak height in the spent catalyst indicated chemical-structural degradation of the support to some amorphous state and conversion of metallic nickel into some non-metallic amorphous material probably nickel hydroxide.

Fig.10 Effect of applied magnetic field on magnetic moments

LEGENDS FOR THE FIGUREStandard Nickel; Spent catalyst

C) Magnetic measurements: The magnetization curves for standard Ni, Ni/Kieselguhr catalyst, (Ni, W, Cu)/Kieselguhr catalyst and spent (Ni, W, Cu/Kieselguhr) catalyst are shown in “Figure 10”. Electron microscopy studies revealed an average Ni, particle size of less than 500 A° in (Ni, W, Cu/Kieselguhr) catalyst. However, isolated particles smaller than this size should normally not exhibit saturation at room temperature. The saturation in the present case, therefore, appears to be due to nickel particles lying very close as a result of high nickel loading.

Fig.11 Effect of repeated use of catalyst on various products distribution during sucrose Hydrogenolysis

Glucose; Fructose; Sorbitol;

Glycerol; Propylene Glycol;

Ethylene Glycol; Sucrose


0

1

2

3

4

5

6

7

8

0

5

10

15

20

25

30

35

40

45

50

1 2 3

% Yield

Run Number


Effectiveness of successive use of (Ni, W, Cu)/kieselguhr catalyst was tested by using thrice to examine the effectiveness of its repeated use during sucrose hydrogenolysis. The catalyst was activated under hydrogen current at 600°C for 2 hours every time it was reused. “Figure 11” demonstrated product distribution obtained after each use of the catalyst at 240 min. reaction time. The catalyst did not loose much of its activity even after two runs as far as sucrose conversion was concerned. Glycerol and glycol yields however dropped appreciably; therefore the catalyst’s product selectivity was unacceptable after its second use. 4. CONCLUSIONIt may be concluded that the process for maximum glycerol yield from catalytic hydrogenolysis of sucrose can effectively be optimized using response surface methodology with a minimum number of experiments. Computerized computations, model building and generation of three-dimensional graphs will go a long way to unravelling the complexity of the preparation of catalyst for glycerol production with the different variables used. The maximum glycerol yield of 43.85% was obtained at 161°C and 48.4 atm pressure with 14.8 % sucrose concentration and 12.23 % catalyst concentration. The work presented here paves the way to synthesize a commercial catalyst to produce various polyols, particularly glycerol, by hydrogenolysis of sucrose.5. ACKNOWLEDGEMENTS

The authors wish to acknowledge the financial support given by the Department of Science and Technology, New Delhi (Govt. of India), to carry out these studies.

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[16] T. Bhaskar, K. R. Reddy, C. P. Kumar, M. R. V. S. Murthy, K. V. R. Charya, “Characterization and Reactivity of Molybdenum Oxide Catalysts Supported on Zirconia,” Appl. Catal. A, vol. 211, pp. 189-201, 2001.

[17] D. Srivastava, R. Garg, P. Kumar, D. C. Saxena, G. N. Mathur, “Optimization studies of blend composition and ageing parameters for making LDPE/HDPE/LLDPE films by response surface methodology,” Macromolecular Materials and Engineering, vol. 283, pp. 1-7, 2000.

[18] D. Srivastava, D. C. Saxena, G. N. Mathur, “Optimization studies on the development of nylon-6 films with high tensile strength,” J. Macromolecular Sci. -Pure and Appl. Chem, vol. A36, pp. 1491- 1501, 1999.

[19] A. M. Joglekar, A. T. May, “Product excellence through design of experiments,” Cereal Foods World, vol. 32, pp. 857, 1987.

[20] G. C. Bond, “Catalysis by Metals,” Academic Press: London, pp. 395, 1962.

[21] F. W. Changa, M. T. Tsay, S. P. Liang, “Hydrogenation of CO2 over nickel catalysts supported on rice husk ash prepared by ion exchange,” Appl. Catal. A: General, vol. 209, pp. 217 – 227, 2001.



[22] M. Arai, Y. Ikushima, Y. Nishiyama, “Pretreatments of Porous Silica for Improving the Activity of a Nickel-Loaded Catalyst,” Bull. Chem. Soc. Jpn.,vol. 59, pp. 347 – 350, 1986.

[23] Tanuja Srivastava, “Studies on the Effect of Various Process Variables and Catalyst Constituents on Hydrogenolysis of Sucrose,” Ph.D. Thesis, Kanpur University, Kanpur, India, 1996.

[24] T. Srivastava, D. C. Saxena, “Optimization of constituents of (Ni,MO,Cu)/kieselguhr catalyst by response surface methodology for Glycerol

production by hydrogenolysis of sucrose,” International Journal of Computers & Technology, vol. 2, pp. 24-40, 2012.

[25] T. Srivastava, D. C. Saxena, “Process optimization of (Ni,MO,Cu)/kieselguhr catalyst by Response surface methodology for Glycerol production by hydrogenolysis of sucrose,” International Journal of Engineering Science & Technology, vol. 3, pp. 7773-7788, 2011.

Curriculum Vitae of Dr. Tanuja Srivastava

Dr. Tanuja Srivastava, Director of BGIET, did her Ph.D. from the Deptt. of Chemical Engg. of Harcourt Butler Technological Institute, Kanpur. She did the post doctoral work in the Chemical Engg. Department of Indian Institute of Technology, Kanpur and Food Engg. Department of Central Food Technological Research Institute, Mysore. Dr. Tanuja Srivastava has teaching and research experience of about 13 years in reputed Engineering Institutes, namely, National Institute of Engineering, Mysore , HBTI, Kanpur & SLIET Longowal. She has in her credit more then 25 publications published in various national and international journals. The research work at HBTI, Kanpur has been filed for 4 patents to Department of Science & Technology, Govt. of India. Totally based on her Ph.D work an industry Girda Chemicals, Mumbai has been established and producing product “Polyols mainly gylcerols from sugar cane juice and qualitatively competing with multinational companies. Previously, it being produced from petroleum sources. Also she has been awarded research fellowship by the Department of Science & Technology, New Delhi and Council of Scientific & Industrial Research, New Delhi. She is working as Principal Co-investigator of the project entitled “Study of Characterization and Treatment of Effluents from Electroplating Industries” sponsored by Ministry of Environmental and Forests, Govt. of India with sanctioned amount of Rs.12.65 lakhs. As Principal Investigator of the project entitled “Production of Polyols by Catalytic Hydrogenolysis of Starch/Molasses in presence of Rice Husk Ash Based Catalysts”. A Ph.D. student is registered for his Ph.D. degree under her supervision. She has an affiliation to various professional bodies, namely, Association of Food Scientists & Technology, Society of Biological Chemists, Indian Chemical Society, Council of Scientific and Industrial Research, Asian Journal of Chemists and Indian Society of Technical Education. She has attended a number of short-term courses and attended various national and international conferences. She is member of Examination Committee, Sports Committee and Cultural Committee of PTU, Jalandhar.

She has over twenty years of experience in teaching Research/Administration of university and IIT level. Out of this rich experience investigator has 9.5 year experience at the professor level and 10 years as a Director, BGIET, Sangrur applicant Institution of she has also liaised with Bureau of Indian Standard research workers, quality marking agencies.

She has also conducted various adequate surveys and deliver many lectures in various seminars. Liaised with burro of Indian Stranded research workers, quality making agencies. Implemented various methods to develop relation between the students teaching staff and admissive staff. Capable of teaching subject such as pharmaceutical chemistry engineering chemistry natural products sensing Evolutions Packaging Engg. Environmental Engg. Provided over all guidance to the faculty for accreditation status to the institution to all five branches. Helped in providing the assistance in opening of five institutions in the field of engineering, Management, Polytechnic as will as inspection from university of or board like BGIET, BGCL, Global Polytechnic Business School.

Opened new student chapter Indian Society for Technical Education New Delhi & Institute of Engineering India, Kolkata. Organized national conference under ISTE chapter, continuously as convener of IDEA 1, 2, 3, 4, 5. She also signed MoUs with International university Wayne Asian Institute of Techonolgy (AIT), Bankok, Thailand, Pittsburg State University, Kansas and Indiana Wesleyan Online University under the student & faculty exchange program. 25 students from Nigria Underwent summer training program at BGIET, Sangrur. Teaching to UG and PG students and guiding theses, research work.

Curriculum Vitae of Dr.D.C.Saxena

Dr. D.C. Saxena, presently Professor and Dean (Planning & Development) is dedicated to the continuing development and practice of creative teaching, innovative research and high impact public service programs that have improved food safety, food quality and processing. He has been a visiting Faculty of Asian Institute of Technology, Bangkok (Thailand). He has been invited to many countries like



Thailand (twice), Germany (twice), Switzerland, Hungary, U.K., Australia, France and USA to present his research work in International Symposia. He has been awarded a prestigious “Prof. Jiwan Singh Sidhu Award” for Best Teacher by AFST (I) India. He is actively engaged in providing technical training to student community and technical services to industry personnel around the region. Various research projects sponsored by GOI funding agencies like ICAR and CSIR, MHRD etc. have been undertaken to attain technical competency and to transfer the technology to the interested industrialists. He has developed two innovative machines viz., Amla Pricking Machine and Continuous Tandoori Roti Baking Oven which has been much sought after by industries (however, these machines would be provided to the industries only after Patenting the technology).

His areas of interest for research include utilization of starches for food and non-food applications from non-conventional sources, traditional product technology (especially in Tandoori roti), dough rheology, grain quality assessment, response surface methodology, design and fabrication of food processing equipments as evident from his technical publications (over 90) in reputed journals and symposia besides reviewing several technical papers in national and international Journals. He has been instrumental in developing his department as a resource centre for Institutions like Panjab University, PAU, Ludhiana and local industries as they come for expert advice and using the laboratory facilities. He has developed a well-equipped laboratory (Food Rheology) for basic and applied research. He has contributed a lot to his field through the following methods:

1. Food processing industries –institute interaction in Punjab region.2. Sharing of facilities (equipment and personnel) with nearby industries and the departments.3. Utilization of facilities of Govt. Institutions viz; CIPHET Ludhiana, Punjab4. University Chandigarh, and PAU Ludhiana for quality research and process development.5. Technology transfer to various industries.6. Imparting expert advice to industries in the vicinity of the institute and also render laboratory facilities

to analyze the samples/products.7. Research projects on innovative ideas.8. Student projects on industrial problems.9. Dissemination of knowledge and expertise through extension services.10. Basic and applied research in food processing, preservation and storage.11. Training to industry personnel.


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